We investigated after effects of automation in take-over scenarios in a high-end moving-base driving simulator. Drivers performed evasive manoeuvres encountering a blocked lane in highway driving. We compared the performance of drivers 1) during manual driving, 2) after automated driving with eyes on the road while performing the cognitively demanding n-back task, and 3) after automated driving with eyes off the road performing the visually demanding SuRT task. Both minimum time to collision (TTC) and minimum clearance towards the obstacle disclosed a substantial number of near miss events and are regarded as valuable surrogate safety metrics in evasive manoeuvres. TTC proved highly sensitive to the applied definition of colliding paths, and we prefer robust solutions using lane position while disregarding heading. The extended time to collision (ETTC) which takes into account acceleration was close to the more robust conventional TTC. In line with other publications, the initial steering or braking intervention was delayed after using automation compared to manual driving. This resulted in lower TTC values and stronger steering and braking actions. Using automation, effects of cognitive distraction were similar to visual distraction for the intervention time with effects on the surrogate safety metric TTC being larger with visual distraction. However the precision of the evasive manoeuvres was hardly affected with a similar clearance towards the obstacle, similar overshoots and similar excursions to the hard shoulder. Further research is needed to validate and complement the current simulator based results with human behaviour in real world driving conditions. Experiments with real vehicles can disclose possible systematic differences in behaviour, and naturalistic data can serve to validate surrogate safety measures like TTC and obstacle clearance in evasive manoeuvres.

With the growing interest in converting a wide variety of biomass-based feedstocks to biofuels, there is a need to develop effective procedures for the design and optimization of multi-feedstock biorefineries. The unifying goal of this work is the development of systematic methodologies and procedures for designing flexible multifeedstock biorefineries. This work addresses four problems that constitute building blocks towards achieving the unifying goal of the dissertation. The first problem addresses the design and techno-economic analysis of an integrated system for the production of biodiesel from algal oil. With the sequestration of carbon dioxide from power plant flue gases, algae growth and processing has the potential to reduce greenhouse gas emissions. Algae are a non-food oil feedstock source and various pathways and technologies for obtaining algal oil were investigated. Detailed economic and sensitivity analysis reveal specific scenarios that lead to profitability of algal oil as an alternative feedstock. In the second problem, a new safety metric is introduced and utilized in process design and selection. A case study was solved to assess the potential of producing biodiesel from sewage sludge. The entire process was evaluated based on multiple criteria including cost, technology and safety. The third problem is concerned with incorporating flexibility in the design phase of the development of multi-feedstock biofuel production processes. A mathematical formulation is developed for determining the optimal flexible design for a biorefinery that is to accommodate the use of multiple feedstocks. Various objective functions may be utilized for the flexible plant depending on the purpose of the flexibility analysis and a case study is presented to demonstrate one such objective function. Finally, the development of a systematic procedure for incorporating flexibility and heat integration in the design phase of a flexible feedstock production process is introduced for the fourth problem. A mathematical formulation is developed for use in determining the heat exchange network design. By incorporating the feedstock scenarios under investigation, a mixed integer linear program is generated and a flexible heat exchange network scheme can be developed. The solution provides for a network that can accommodate the heating and cooling demands of the various scenarios while meeting minimum utility targets.